Deep Learning Model for Automatic Classification and Prediction of Brain Tumor. (8th April 2022)

• Record Type: Journal Article
• Authors: Sharma, Sarang; Gupta, Sheifali; Gupta, Deepali; Juneja, Abhinav; Khatter, Harsh; Malik, Sapna; Bitsue, Zelalem Kiros
• Other Names: Singh Pradeep Kumar Academic Editor.
• Is Part Of: Journal of sensors. Volume 2022(2022)
• Publication Date: 2022-04-08
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2022/3065656
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : A brain tumor (BT) is an unexpected growth or fleshy mass of abnormal cells. Depending upon their cell structure they could either be benign (noncancerous) or malign (cancerous). This causes the pressure inside the cranium to increase that may lead to brain injury or death. This causes excessive exhaustion, hinders cognitive abilities, headaches become more frequent and severe, and develops seizures, nausea, and vomiting. Therefore, in order to diagnose BT computerized tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and blood and urine tests are implemented. However, these techniques are time consuming and sometimes yield inaccurate results. Therefore, to avoid such lengthy and time-consuming techniques, deep learning models are implemented that are less time consuming, require less sophisticated equipment, yield results with greater accuracy, and are easy to implement. This paper proposes a transfer learning-based model with the help of pretrained VGG19 model. This model has been modified by utilizing a modified convolutional neural network (CNN) architecture with preprocessing techniques of normalization and data augmentation. The proposed model achieved the accuracy of 98% and sensitivity of 94.73%. It is concluded from the results that proposed model performs better as compared to other state-of-art models. For training purpose, the dataset has been taken from the Kaggle having 257 images with 157 with brain tumor (BT) images and 100 no tumor (NT) images. With such results, these models could be utilized for developing clinically useful solutions that are able to detect BT in CT images.